Data Mining Processes, Advantages, and Risks

Introduction

Data mining processes have recently gained popularity due to the extensive use of big data in organizations. Data mining is a technique employed to extract useful information from the numerous data sets of big data. Data mining employs various methods, including supervised and unsupervised learning models. The supervised learning model entails algorithms that retrieve information from known data sets, while the unsupervised method is implemented when mining useful information from unknown data sets (Jassim & Abdulwahid, 2021). Alternatively, organizations will use artificial intelligence while others will use statistical methods like regression to identify patterns in large complex data. Numerous processes take place using data mining techniques. First, the data scientists and analysts identify the data source from which information will be extracted. The source can be a database, static file, abstract web data, or live measurements from a physical system. The acquired information is then cleaned to remove corrupt, redundant, misformatted, and inaccurate data. Data mining techniques are then applied to integrate information from various sources and identify useful patterns and relationships to the organization. The useful information is then transformed into the format used for storage. The mined information is then presented to relevant stakeholders. The literature review will describe some challenges encountered in data mining processes, an organization that has dealt with such challenges, potential solutions, and recommendations for the best solution.

Literature Review

Problems Associated with Data Mining Processes

Jassim & Abdulwahid (2021) identifies the challenge of working with too many components oriented toward a specific goal. One of the data mining processes is identifying relevant data sources. Some sources include databases that describe massive structured data stored in a computer system. Another source is a live measurement of a physical system. Live measurements imply real-time data collection at a high velocity. An organization may opt to extract data static files like images and scripts when they are crucial to the task at hand. Regardless of the organization’s data source, data mining techniques are implemented to clean the data and identify patterns. Pattern identification encompasses filtering through the collected information, analyzing and identifying data variables then relating them. Therefore, using many sources with unrelated components and finding patterns is challenging (Dayalan, 2020). Data miners struggle to obtain the relationships between unrelated components and use them to extract valuable information to perform a particular task like informing business decisions.

Another challenge with implementing data mining processes is that the information extracted may be insufficient. For instance, real-time data streaming provides a snapshot of a system that is insufficient to provide an overview of all operations in the system (Dayalan, 2020). Such systems are dynamic such that mined information will give an inaccurate insight into their functions. When data miners draw conclusions based on that information, they provide undesirable outcomes. Besides live sources, mining data from unreliable sources is misleading. For instance, data from a particular website can be forced, and the data miners may fail to identify the corruptness when cleaning it. As a result, the information extracted from these sources will provide conclusions that are far from the actual situation. Jassim & Abdulwahid (2021) further supports that the diversity of big data makes identifying the value of information challenging. So many variables are at play that may not collaborate in forming the reality of the situation. The output of the mining processes will provide insufficient predictions that will adversely impact an organization’s business decisions.

The issue of proper technological instruments for mining processes affects many organizations. Although many organizations have digitized many of their systems, technological advancements are rapid and difficult to keep up with. Therefore, an organization without the most advanced technology will not know about efficient algorithms that empower data mining processes (Jassim & Abdulwahid, 2021). Likewise, some organizations use network systems with low speed. However, the efficiency of data mining processes depends on speed; thus, organizations with slow networks are unlikely to extract useful information from the collected data. The issue of speed is critical, especially when dealing with real-time data where information’s value is time sensitive. Additionally, improper technological tools present the issue of cyber threats during data collection (Dayalan, 2020). Some data sources may harbor security incidences such as malware which, if not detected on time, will disrupt the data mining processes leading to incorrect output. From another perspective, an organization that has automated its operations will not have incorporated the technology in data mining which setbacks the efficiency of the information extraction processes. Overall, incorporating the correct technological tools in the data mining processes is critical in the extraction of valuable information.

According to R et al. (2018), overfitting is a major issue in data mining. Overfitting refers to the use of incompatible techniques in data mining processes. It can also describe the use of data models that do not apply to future situations. Data models employ various algorithms to search and sort vast data to identify the most significant parameters. Every model has its own defining features and outcomes. Therefore, an organization can implement a data model in mining processes that cannot provide insight into the future. Using such a model whose parameters do not fit the future states to make prescriptive predictions results in errors that negatively impact the organization. Similarly, using a data model that generalizes the future with now without considering unforeseen changes and incorporating them will also provide inaccurate conclusions (Jassim & Abdulwahid, 2021). Moreover, due to the velocity and variability of data, a data model used by data miners may become obsolete before its predictions become useful, making them misfitting to future states. Overall, overfitting is a common challenge in data mining operations.

Organization That has Dealt with Challenges Facing Data Mining Processes

Amazon integrates data mining in its operations and has dealt with the challenges associated with the technique. Specifically, Amazon implements the data mining software called SageMaker. The Amazon SageMaker comprised various tools that empower data mining processes. One of SageMaker’s tools is Data Wrangler, which speeds up the proceeding time for big data from days to minutes. Data Wrangler thus overcomes the challenge of processing real-time data where speed is vital. Another tool of SageMaker is Studio. The Studio describes a cloud-based visual interface through which Amazon data scientists can develop machine learning algorithms. Machine learning algorithms allow data scientists to mine information at high speed, which increases their productivity (Biswas et al., 2021). Overall, the SageMaker studio allows data scientists to track every development process of the data mining models. Also, SageMaker has distributed training libraries containing partitioning algorithms that split the large data into smaller data sets that other data models can sort efficiently (Zamri et al., 2020). Likewise, Amazon overcomes the challenge of inaccurate predictions using the tool Debugger which utilizes real-time metrics to identify anomalies with predictions and correct them. Therefore, the product SageMaker helps Amazon mine data widely and efficiently.

Major Challenges in Data Mining

One of the biggest issues in the data mining process is incomplete data. Sufficient data is crucial in drawing patterns and associations in data mining (Sadineni, 2020). Missing data results in errors and uncertainties in the analyzed data. Although the data mining algorithms may attempt to replace the missing components and variables using established patterns, data scientists still struggle to find conclusive results. Another major issue is data heterogeneity (Dayalan, 2020). The organization’s data is collected from various sources in different formats. Harmonizing this data towards a specific task is challenging for data scientists. Moreover, the vastness and complexity of data make data mining processes cumbersome to manage. The aforementioned factors represent the biggest issues in data mining technology.

Possible Solutions to the Aforementioned Challenges

R et al. (2018) suggest some solutions for data mining challenges. For instance, revising the data models currently being used to mine data to fix the incompatibilities will eliminate outliers and misfits. It will also promote the accuracy of predictions. Another solution is designing incremental data mining methods that will overcome the current challenge of rapidly changing and accumulating data. Moreover, developing new data mining algorithms addressing the deficiencies with the current ones will overcome the limitation of using obsolete data models. Moreover, developing a unifying theory of data models will bring homogeneity in data mining techniques and lower the chance for some organizations to be left behind (Dayalan, 2020). Implementing one or more of the above solutions will help an organization overcome deficiencies in data processing.

Recommended Best Solution and its Implementation

Developing a unifying theory is recommended for the challenges facing data mining processes. Lack of a unified theoretical framework to guide the data mining processes has caused there to have chaotic data models (Plotnikova et al., 2020). Organizations develop data models oriented towards solving a particular problem in data mining processes leading to disunity in the field. Having a single framework standardizes the data mining processes and unites different approaches organizations use to mine data (Mamudu et al., 2023). Moreover, with a unified theory to guide data mining processes, organizations can focus on navigating how to overcome other challenges like outliers, missing data, and heterogeneity.

Furthermore, developing a unified theory will help the data mining field deepen its knowledge of managing big data. Big data is voluminous and has high velocity; many data models cannot keep up with it. Although organizations have developed a multitude of data models, they have low efficiency when dealing with big data. Therefore, uniting the field of data mining to provide a single solution that captures its current challenges will deepen the knowledge base of many data miners (Mamudu et al., 2023). The expanse in knowledge will arise from the need to create viable solutions. Other benefits of a unified framework will comprise; high processing speed, reinforced security measures, and cost-effective data modeling (Plotnikova et al., 2020). Overall, a unified theoretical framework to inform data mining processes with enhance and revolutionize the technique. More than that, organizations’ ability to manage big data will significantly improve.

Implementing the unified theoretical framework approach will involve improving existing knowledge and combining it to form a composite single-step process. The numerous algorithms data scientists have developed to solve specific problems in data mining will combine to form one data model (Plotnikova et al., 2020). Moreover, statistical, artificial intelligence and machine learning will combine to form a unified set of rules, processes, and algorithms to mine data (Plotnikova et al., 2020). In addition to scaling existing data models, the unified approach will also improve interpretation and data visualization methods. As a result, a clear distinction between data analysis and mining will form.

Conclusion

Data mining processes are crucial to extracting valuable information from big data. The processes consist of data models and algorithms which sort out the vast big data and identify valuable data sets that can perform a particular goal. Despite their benefits, data mining processes face many challenges. Such challenges include; vast and complex solutions, outfitting future states, insufficient technological know-how, and insufficient data. These challenges can be overcome by revising current data models and algorithms and uniting them into a single-step composite framework for data mining. Such an approach will compensate for the challenges data miners face when extracting valuable information from lots of data.

References

Biswas, B., Sanyal, K., & Mukherjee, T. (2021, December). (PDF) web data mining: Sentiment analysis of Amazon product – researchgate. https://www.researchgate.net/profile/Biswajit-Biswas-19/publication/363862629_Web_Data_Mining_Sentiment_Analysis_of_Amazon_Product/links/6332d50f694dbe4bf4c6480a/Web-Data-Mining-Sentiment-Analysis-of-Amazon-Product.pdf

Dayalan, M. (2020, June). Top challenges in Data Mining Research. JETIR. https://www.jetir.org/view?paper=JETIR1903264

Jassim, M. A., & Abdulwahid, S. N. (2021). Data mining preparation: Process, techniques, and major issues in data analysis. IOP Conference Series: Materials Science and Engineering, 1090(1), 012053. https://doi.org/10.1088/1757-899x/1090/1/012053

Mamudu, A., Bandara, W., Leemans, S. J. J., & Wynn, M. T. (2023). Process mining impacts framework. Business Process Management Journal, 29(3), 690–709. https://doi.org/10.1108/bpmj-09-2022-0453

Plotnikova, V., Dumas, M., & Milani, F. (2020). Adaptations of data mining methodologies: A systematic literature review. PeerJ Computer Science, 6. https://doi.org/10.7717/peerj-cs.267

R, R., B, S., & Sofia, V. S. (2018). Data Mining Issues and Challenges: A Review. IJARCCE, 7(11), 118–121. https://doi.org/10.17148/ijarcce.2018.71125

Sadineni, P. K. (2020, November 25). Mining in big data: Challenges, solutions. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3734166

Zamri, N. E., Mansor, Mohd. A., Mohd Kasihmuddin, M. S., Alway, A., Mohd Jamaludin, S. Z., & Alzaeemi, S. A. (2020). Amazon Employees Resources Access Data Extraction via clonal selection algorithm and Logic Mining Approach. Entropy, 22(6), 596. https://doi.org/10.3390/e22060596